Many techniques have been proposed for performing machine recognition of the content of spoken words. One highly regarded technique is described in an article by Lawrence R. Rabiner, entitled "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition", Proceedings of the IEEE, vol. 77, no. 2, February, 1989, pp. 257-286. According to this technique, spoken utterances, comprising either continuous speech or isolated words or phrases, are modeled in terms of state transitions by statistical models known as hidden Markov models. Although this technique has provided promising results in terms of accuracy of recognition, the models must be quite complex if satisfactory performance is to be achieved, so that a large number of calculations must be performed during recognition operations. In addition, the required calculations often cannot be satisfactorily performed using fixed point processing. Because fixed point processors are relatively low in cost, and are commonly used in many types of office equipment, it would be desirable to provide a speech recognition technique that can be performed satisfactorily with a fixed point processor while achieving a degree of accuracy that is comparable to that provided by hidden Markov model techniques.